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  1. Why Worry about Evolution? Boundaries, Practices, and Moral Salience in Sunni and Evangelical High Schools

    Previous work on conservative Protestant creationism fails to account for other creationists who are much less morally invested in opposition to evolution, raising the sociological question: What causes issues’ moral salience? Through ethnographic fieldwork in four creationist high schools in the New York City area (two Sunni Muslim and two conservative Protestant), I argue that evolution is more important to the Christian schools because it is dissonant with their key practices and boundaries.

  2. ASA Continues to Respond to the Changing Climate for Sociologists in America

    ASA has a long and ongoing history of activity supporting diversity, inclusion, free inquiry, and academic freedom.  The need for such activity has escalated in recent weeks in deeply troubling ways, with developments ranging from a rash of racist, xenophobic, and other forms of discriminatory activities on campuses across the nation to the introduction of the Professor Watchlist which

  3. Traditional, Modern, and Post-Secular Perspectives on Science and Religion in the United States

    Using General Social Survey data, we examine perspectives on science and religion in the United States. Latent class analysis reveals three groups based on knowledge and attitudes about science, religiosity, and preferences for certain religious interpretations of the world. The traditional perspective (43 percent) is marked by a preference for religion compared to science; the modern perspective (36 percent) holds the opposite view. A third perspective, which we call post-secular (21 percent), views both science and religion favorably.

  4. Featured Essay: The Arrival of Social Science Genomics

    “The genetics revolution may be well underway,” write Dalton Conley and Jason Fletcher in The Genome Factor, “but the social genomics revolution is just getting started” (p. 11). They are not alone in their excitement for recent developments bringing together social science and genetic research. Decades from now, folks may well look back at this time as the start of a golden age for the field.
  5. Nonlinear Autoregressive Latent Trajectory Models

    Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed.
  6. Comment: Bayes, Model Uncertainty, and Learning from Data

    The problem of model uncertainty is a fundamental applied challenge in quantitative sociology. The authors’ language of false positives is reminiscent of Bonferroni adjustments and the frequentist analysis of multiple independent comparisons, but the distinct problem of model uncertainty has been fully formalized from a Bayesian perspective.
  7. We Ran 9 Billion Regressions: Eliminating False Positives through Computational Model Robustness

    False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used.
  8. Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and Probit: A New Method

    Logit and probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted to the same sample does not warrant the same interpretation in logits and probits as in linear regression. Unlike linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models.

  9. Text Analysis with JSTOR Archives

    I provide a visual representation of keyword trends and authorship for two flagship sociology journals using data from JSTOR’s Data for Research repository. While text data have accompanied the digital spread of information, it remains inaccessible to researchers unfamiliar with the required preprocessing. The visualization and accompanying code encourage widespread use of this source of data in the social sciences.

  10. Visualizing Belief in Meritocracy, 1930–2010

    In this figure I describe the long trend in popular belief in meritocracy across the Western world between 1930 and 2010. Studying trends in attitudes is limited by the paucity of survey data that can be compared across countries and over time. Here, I show how to complement survey waves with cohort-level data. Repeated surveys draw on a representative sample of the population to describe the typical beliefs held by citizens in a given country and period.